# Dialogue Response Ranking Training with Large-Scale Human Feedback Data

arXiv: https://arxiv.org/abs/2009.06978 · HTML: https://mlsanity.com/paper/2009.06978/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 15 Sep 2020.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 21 of 21)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https://mlsanity.com/paper/1809.05972/) (2018)
- [Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space](https://mlsanity.com/paper/2004.04092/) (2020)
- [ConveRT: Efficient and Accurate Conversational Representations from Transformers](https://mlsanity.com/paper/1911.03688/) (2019)
- [A Repository of Conversational Datasets](https://mlsanity.com/paper/1904.06472/) (2019)
- [Structuring Latent Spaces for Stylized Response Generation](https://mlsanity.com/paper/1909.05361/) (2019)
- [Jointly Optimizing Diversity and Relevance in Neural Response Generation](https://mlsanity.com/paper/1902.11205/) (2019)
- [Predicting User-Interactions on Reddit](https://mlsanity.com/paper/1707.00195/) (2017)
- [MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform](https://mlsanity.com/paper/2005.08365/) (2020)
- [Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News](https://mlsanity.com/paper/1501.07860/) (2015)

## Built on by (most cited first, 30 of 68)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [A Comprehensive Assessment of Dialog Evaluation Metrics](https://mlsanity.com/paper/2106.03706/) (2021)
- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach](https://mlsanity.com/paper/2104.01767/) (2021)
- [Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://mlsanity.com/paper/2412.09416/) (2024)
- [X-Risk Analysis for AI Research](https://mlsanity.com/paper/2206.05862/) (2022)
- [Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey](https://mlsanity.com/paper/2503.22458/) (2025)
- [The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values](https://mlsanity.com/paper/2310.07629/) (2023)
- [Shades of BLEU, Flavours of Success: The Case of MultiWOZ](https://mlsanity.com/paper/2106.05555/) (2021)
- [InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning](https://mlsanity.com/paper/2205.12673/) (2022)
- [The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues](https://mlsanity.com/paper/2306.06941/) (2023)
- [Spurious Correlations in Reference-Free Evaluation of Text Generation](https://mlsanity.com/paper/2204.09890/) (2022)
- [CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering](https://mlsanity.com/paper/2506.08584/) (2025)
- [Customizing Language Model Responses with Contrastive In-Context Learning](https://mlsanity.com/paper/2401.17390/) (2024)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors](https://mlsanity.com/paper/2507.10579/) (2025)
- [Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models](https://mlsanity.com/paper/2305.14718/) (2023)
- [On Zero-Shot Counterspeech Generation by LLMs](https://mlsanity.com/paper/2403.14938/) (2024)
- [Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach](https://mlsanity.com/paper/2102.10242/) (2021)
- [PrefPalette: Personalized Preference Modeling with Latent Attributes](https://mlsanity.com/paper/2507.13541/) (2025)
- [Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges](https://mlsanity.com/paper/2203.10012/) (2022)
- [FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation](https://mlsanity.com/paper/2210.13832/) (2022)
- [AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence](https://mlsanity.com/paper/2404.11826/) (2024)
- [ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions](https://mlsanity.com/paper/2407.02472/) (2024)
- [Modulating Language Models with Emotions](https://mlsanity.com/paper/2108.07886/) (2021)
- [Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization](https://mlsanity.com/paper/2109.07968/) (2021)
- [Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations](https://mlsanity.com/paper/2109.13087/) (2021)
- [Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources](https://mlsanity.com/paper/2311.07589/) (2023)

## Abstract (arXiv)

Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conversational models are increasingly capable of producing turns that are context-relevant, but in order to produce compelling agents, these models need to be able to predict and optimize for turns that are genuinely engaging. We leverage social media feedback data (number of replies and upvotes) to build a large-scale training dataset for feedback prediction. To alleviate possible distortion between the feedback and engagingness, we convert the ranking problem to a comparison of response pairs which involve few confounding factors. We trained DialogRPT, a set of GPT-2 based models on 133M pairs of human feedback data and the resulting ranker outperformed several baselines. Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback. We finally combine the feedback prediction models and a human-like scoring model to rank the machine-generated dialog responses. Crowd-sourced human evaluation shows that our ranking method correlates better with real human preferences than baseline models.